
modifying the training data to make it more balanced (e.g., by up- or down-sampling different labels [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}),

modifying the classification rule to ensure greater representation of rare classes (e.g., applying per-class thresholds [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}),
and

modifying the loss function to penalise errors on rare labels more strongly (e.g., by introducing appropriate asymmetry [16]}, [17]}, [18]}, [19]}, [20]}, [21]}, [22]}, [23]}, [24]}, [25]}, [26]}).

